Do the numbers stack up?

Is Sports Analytics Really Worth the Price Tag?

The “eye test” was once the gold standard in sports. Experts trusted their intuition. Fully. And why shouldn’t they? Would a person who spent a career playing and observing baseball, for example, not know a good batter when he saw one? Not quite, as it turns out.

So long as all experts shared the same intuition, all was well. The consensus only reinforced expert self-confidence. Until, that is, a handful of highly ambitious and poorly resourced sports managers – with no choice but to buck the trend – began using data and analytics to exploit the prevalent biases and errors, and quickly brought the “eye ball” edifice crashing down.

The story of the Oakland A’s – the team whose heterodox recruitment choices upended Major League Baseball – was brought to life in Michael Lewis’s bestseller – Moneyball. Having to recruit players on a tight budget, General Manager Billy Beane had little choice but to use analytics in an original way to identify talent that went underappreciated by scouts and therefore undervalued on the market. He successfully assembled a sensational winning team on a shoestring.

From the Moneyball revolution of the early 2000s to the sophisticated player-tracking data used in the NBA and Premier League today, data has moved from the sidelines to the centre of the strategy room. But for many sporting clubs – especially those outside the global elite tiers – the question remains: is a heavy investment in sports analytics actually worthwhile?

The Case for the Spend

The primary argument for investing in analytics is the optimisation of resources. It is truism in national security that intelligence is a “force multiplier”. The same holds true in sports too.

For most professional sporting clubs, the largest expense is the playing squad. Analytics makes the process of recruiting players cheaper and much more effective. Team managers and their scouts can quickly and cheaply sift through a massive international field of potential candidates, create an effective shortlist, and then pick the likely best choice for the club’s needs. By using data to scout the global markets and the lower division, clubs can unearth cheaper talent and compete with more affluent rivals not by outspending them but by outsmarting them.

Beyond recruitment, analytics is vital for performance optimisation and injury prevention. Wearable technology and biometric data allow coaches to monitor player workloads in real time. By identifying the “red zones” of fatigue, clubs can tailor training sessions to individual needs – significantly reducing the frequency of soft-tissue injuries and making training more effective. In a season where a single injury to a star player can derail a championship run, the return on investment on injury prevention alone can justify the cost of an entire data department.

The Challenges of Implementation

Implementing the data revolution is not trivial. Not financially, not organisationally, and not culturally.

Data, never speaks for itself, and never tells the full story. Some important aspects of team sports – such as team dynamics – are notoriously hard to measure. No amount of performance measurement of legendary soccer player, Diego Maradona, in the twilight of his career could have captured his catalytic effect on the Argentinian national soccer team when he came off the bench. Both Argentinian coaches and coaches competing against Argentina would have had to go well beyond analytics to properly factor Maradona and his influence into their plans. So analytics is no replacement for coaching judgement and strategic thinking, but rather an additional source of information that needs to be processed and ingested on top of everything else.

The use of analytics introduces an added risk of misinterpretation. Bill James, the father of sabermetrics, had challenged the over-reliance on batting averages for decades. But it was not until Oakland A’s’ iconoclastic General Manger, Billy Beane, with his assistant Paul dePodesta broke with orthodoxy and upended American baseball, that practitioners realised they had been misinterpreting the data all along. In its 2001 recruitment the Oakland A’s took the gamble of ditching the batting average in favour of On-Base Percentage (OBP), thereby shifting the focus of batting evaluation from successful batting to plate discipline – the batter’s propensity to avoid swings at bad balls and minimise strike outs. OBP, more than batting average, correlated with scoring runs. The mindless use of analytics had led other baseball teams to squander many millions of dollars by overpaying for suboptimal players, and disregarding true talent.

But the challenges of analytics are greater than the risks of misinterpretation and the extra burden of an additional flood of information. To be useful, analytics must be turned into relevant, timely and actionable intelligence. Decision makers need to get analytics-driven insights in a format they can use at the moment they need it. Recruitment data, for instance, must go beyond raw statistics – it needs to be converted into forecasts: How will specific players function within the team? What gaps will they fill? What vulnerabilities might they introduce?

Only organisations and decision makers that are willing to put in the time and effort to adopt an intelligence mindset can turn analytics into a decision advantage.

The cost is not trivial. A robust analytics department requires investment in high-quality data feeds, specialised software, and, most critically, skilled personnel who can select the right data, interpret the numbers and turn them into timely, relevant insights. And decision makers need to adapt the way they do business.

The Bottom Line

For top clubs in elite sports the cost of analytics is comparatively small. Often no more than about 2% of the total budget. But for smaller clubs operating on tight margins, this can feel like a luxury they can’t afford. But can they really afford to forgo analytics?

Counterintuitively, perhaps, the value of analytics is higher for less affluent clubs. Consider the risks involved in a bad recruitment of an elite player. A large club signing five players can absorb one poor decision – it represents 20% of that cohort. A smaller club making a single marquee signing has everything riding on that one call. The same logic applies to injury prevention: when your bench lacks depth, an injury to a key player can be fateful. When analytics helps make the right scouting call, or prevents a critical injury, the impact is proportionally greater for the club with fewer resources to fall back on.

So, is it worth throwing money at analytics?

For professional and semi-professional clubs the answer is emphatically yes.

Elite clubs cannot afford to be outpaced by their rivals – being on the cutting edge is part of what defines them.

But it is the underdogs, those who seek to defy the odds and punch above their weight, who stand to gain most from a judicious use of analytics. When your rivals are stronger, and you cannot outmuscle them, your only hope is to outsmart them.